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Optimizing fuzzy membership function using dynamic multi swarm — PSO

机译:使用动态多群 - PSO优化模糊会员函数

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Performance of fuzzy application to solve the control problems depends on a number of parameters such as the choice and shape of the membership function. Defining MFs manually in a proper way is time consuming, prone to errors and difficult. And especially it depends subjectively based on expert's experiences. Improvement of the performance of the fuzzy control system is made by the optimization of the membership function. In this paper, a Dynamic Multi-Swarm PSO is used to optimize the fuzzy membership function. DMS-PSO has the ability to avoid local optimal and able to generate an optimal set of parameters for fuzzy control system. The experiment carried out with real-life application, park a vehicle into garage beginning from any starting position; results show that the better performance of proposed fuzzy model is obtained by using the optimized membership functions than a simple fuzzy model when the membership functions were heuristically defined.
机译:模糊应用程序来解决控制问题的性能取决于许多参数,例如隶属函数的选择和形状。以适当的方式手动定义MFS是耗时,容易出错和困难。特别是基于专家的经验,它取决于主观。通过优化隶属函数来提高模糊控制系统的性能。在本文中,使用动态多群PSO来优化模糊隶属函数。 DMS-PSO能够避免局部最佳,能够为模糊控制系统产生最佳参数集。使用现实生活应用进行的实验,从任何起始位置停放车辆进入车库;结果表明,当隶属函数函数在启动性定义时,通过使用优化的隶属函数来获得所提出的模糊模型的更好性能。

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